A deep neural network with invertible hidden layers has a nice property of preserving all the information in the feature learning stage. In this paper, we analyse the hidden layers of residual rectifier neural network...
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A deep neural network with invertible hidden layers has a nice property of preserving all the information in the feature learning stage. In this paper, we analyse the hidden layers of residual rectifier neuralnetworks, and investigate conditions for invertibility under which the hidden layers are invertible. A new fixed-point algorithm is developed to invert the hidden layers of residual networks. The proposed inversealgorithms are capable of inverting some residual networks which cannot be inverted by existing inverting algorithms. Furthermore, a special residual rectifier network is designed and trained on MNIST so that it can achieve comparable performance with the state-of-art performance while its hidden layers are invertible.
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